if (!require("remotes"))
install.packages("remotes")
remotes::install_github("flavjack/inti")ABONOS ORGÁNICOS EN LA CALIDAD Y MANEJO POSTCOSECHA DE MANGO (Mangifera indica L). VAR. KENT
1 Setup
Instalar version en desarrollo.
library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
source('https://inkaverse.com/setup.r')
# library(rstatix)
# library(dlookr)
# library(car)
session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14 ucrt)
os Windows 11 x64 (build 22631)
system x86_64, mingw32
ui RTerm
language (EN)
collate Spanish_Latin America.utf8
ctype Spanish_Latin America.utf8
tz America/Lima
date 2024-07-25
pandoc 3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
agricolae 1.3-7 2023-10-22 [1] CRAN (R 4.4.0)
AlgDesign 1.2.1 2022-05-25 [1] CRAN (R 4.4.0)
askpass 1.2.0 2023-09-03 [1] CRAN (R 4.4.0)
boot 1.3-30 2024-02-26 [2] CRAN (R 4.4.1)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.0)
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.1)
cluster 2.1.6 2023-12-01 [2] CRAN (R 4.4.1)
coda 0.19-4.1 2024-01-31 [1] CRAN (R 4.4.0)
codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.1)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.4.0)
corrplot * 0.92 2021-11-18 [1] CRAN (R 4.4.1)
cowplot * 1.1.3 2024-01-22 [1] CRAN (R 4.4.0)
curl 5.2.1 2024-03-01 [1] CRAN (R 4.4.0)
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.4.0)
digest 0.6.36 2024-06-23 [1] CRAN (R 4.4.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
DT 0.33 2024-04-04 [1] CRAN (R 4.4.0)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.0)
emmeans * 1.10.3 2024-07-01 [1] CRAN (R 4.4.1)
estimability 1.5.1 2024-05-12 [1] CRAN (R 4.4.0)
evaluate 0.24.0 2024-06-10 [1] CRAN (R 4.4.0)
FactoMineR * 2.11 2024-04-20 [1] CRAN (R 4.4.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
flashClust 1.01-2 2012-08-21 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.4.0)
FSA * 0.9.5 2023-08-26 [1] CRAN (R 4.4.1)
gargle 1.5.2 2023-07-20 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.4.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
googledrive * 2.1.1 2023-06-11 [1] CRAN (R 4.4.0)
googlesheets4 * 1.1.1 2023-06-11 [1] CRAN (R 4.4.0)
gsheet * 0.4.5 2020-04-07 [1] CRAN (R 4.4.1)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.4.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
huito * 0.2.4 2023-10-25 [1] CRAN (R 4.4.0)
inti * 0.6.5 2024-07-25 [1] Github (flavjack/inti@38be898)
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
knitr * 1.48 2024-07-07 [1] CRAN (R 4.4.1)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.1)
leaps 3.2 2024-06-10 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
lme4 1.1-35.5 2024-07-03 [1] CRAN (R 4.4.1)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.4.0)
magick * 2.8.4 2024-07-14 [1] CRAN (R 4.4.1)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
MASS * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
Matrix 1.7-0 2024-04-26 [2] CRAN (R 4.4.1)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.0)
minqa 1.2.7 2024-05-20 [1] CRAN (R 4.4.0)
mnormt 2.1.1 2022-09-26 [1] CRAN (R 4.4.0)
multcomp * 1.4-26 2024-07-18 [1] CRAN (R 4.4.1)
multcompView 0.1-10 2024-03-08 [1] CRAN (R 4.4.0)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.0)
mvtnorm * 1.2-5 2024-05-21 [1] CRAN (R 4.4.0)
nlme 3.1-164 2023-11-27 [2] CRAN (R 4.4.1)
nloptr 2.1.1 2024-06-25 [1] CRAN (R 4.4.1)
openssl 2.2.0 2024-05-16 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.4.1)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
psych * 2.4.6.26 2024-06-27 [1] CRAN (R 4.4.1)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0)
Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.1)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.0)
remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.0)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
sandwich 3.1-0 2023-12-11 [1] CRAN (R 4.4.0)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
scatterplot3d 0.3-44 2023-05-05 [1] CRAN (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
shiny * 1.8.1.1 2024-04-02 [1] CRAN (R 4.4.0)
showtext 0.9-7 2024-03-02 [1] CRAN (R 4.4.0)
showtextdb 3.0 2020-06-04 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
survival * 3.6-4 2024-04-24 [2] CRAN (R 4.4.1)
sysfonts 0.8.9 2024-03-02 [1] CRAN (R 4.4.0)
TH.data * 1.1-2 2023-04-17 [1] CRAN (R 4.4.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.0)
usethis * 2.2.3 2024-02-19 [1] CRAN (R 4.4.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.4.0)
xfun 0.46 2024-07-18 [1] CRAN (R 4.4.1)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
yaml 2.3.9 2024-07-05 [1] CRAN (R 4.4.1)
zoo 1.8-12 2023-04-13 [1] CRAN (R 4.4.0)
[1] C:/Users/LENOVO/AppData/Local/R/win-library/4.4
[2] C:/Program Files/R/R-4.4.1/library
──────────────────────────────────────────────────────────────────────────────2 Refrencias
- (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
- (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
- (HCPC) https://youtu.be/EJqYTDTJJug
3 Import data
https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135
url <- "https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135"
gs <- url %>%
as_sheets_id()
tratamiento <- gs %>%
range_read("tratamientos") %>%
rename_with(~ tolower(.))
rendimiento <- gs %>%
range_read("rendimiento") %>%
rename_with(~ tolower(.))
fisio <- gs %>%
range_read("fisio") %>%
rename_with(~ tolower(.)) %>%
merge(., tratamiento) %>%
dplyr::select(tratamiento,compost, biol,everything()) %>%
merge(., rendimiento) %>%
mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>%
rename(treat = tratamiento
, repetition = repeticion
, composts = compost)
str(fisio)
## 'data.frame': 405 obs. of 15 variables:
## $ treat : Factor w/ 9 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ repetition: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ nplantas : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
## $ composts : Factor w/ 3 levels "0","5","15": 1 1 1 1 1 1 1 1 1 1 ...
## $ biol : Factor w/ 3 levels "0","5","10": 1 1 1 1 1 1 1 1 1 1 ...
## $ nfrutos : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## $ pcfmf : num 90 70 60 40 70 70 40 60 30 80 ...
## $ ffmf : num 13.2 12 10 10.2 12.8 11.6 10.8 11.9 11 11.2 ...
## $ cifmf : num 2 2 2 2 2 1.5 2 2 2.5 2.5 ...
## $ ssfmf : num 8.8 8.6 8.5 8.2 8.6 9.8 9.7 9.4 9.2 8.5 ...
## $ phfmf : num 2.6 2.55 2.52 2.58 2.55 ...
## $ atfmf : num 1.39 1.38 1.2 1.12 1.35 1.49 1.29 1.52 1.54 1.42 ...
## $ msfmf : num 19.1 19.1 19.1 19.1 19.1 ...
## $ imf : num 6.33 6.23 7.08 7.32 6.37 6.58 7.52 6.18 5.97 5.99 ...
## $ rpp : num 51.3 51.3 51.3 51.3 51.3 52.4 52.4 52.4 52.4 52.4 ...
consumo <- gs %>%
range_read("consumo") %>%
rename_with(~ tolower(.)) %>%
merge(., tratamiento) %>%
dplyr::select(tratamiento,compost, biol,everything()) %>%
mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>%
rename(treat = tratamiento
, repetition = repeticion
, composts = compost
, n_fruits = nfrutos)
glimpse(consumo)
## Rows: 135
## Columns: 12
## $ treat <fct> T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0,…
## $ composts <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5,…
## $ biol <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ repetition <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1,…
## $ n_fruits <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,…
## $ pdfmc <dbl> 6.68, 6.78, 7.02, 6.78, 6.12, 6.62, 6.91, 7.04, 6.45, 6.50,…
## $ ffmc <dbl> 3.0, 3.0, 4.0, 3.8, 4.2, 4.0, 3.6, 3.0, 3.0, 3.0, 3.0, 3.4,…
## $ cifmc <dbl> 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.0, 3.0, 3.0, 3.5, 3.0,…
## $ ssfmc <dbl> 14.6, 15.6, 14.8, 15.0, 14.0, 14.4, 15.5, 15.1, 15.2, 15.2,…
## $ phfmc <dbl> 4.22, 4.19, 4.21, 4.15, 4.26, 4.34, 4.35, 4.34, 4.30, 4.38,…
## $ atfmc <dbl> 0.600, 0.700, 0.700, 0.500, 0.500, 0.605, 0.615, 0.625, 0.6…
## $ imf <dbl> 24.33333, 22.28571, 21.14286, 30.00000, 28.00000, 23.80165,…4 Data summary
sm <- fisio %>%
group_by(treat) %>%
summarise(across(pcfmf:rpp, ~ sum(!is.na(.))))
sm
## # A tibble: 9 × 10
## treat pcfmf ffmf cifmf ssfmf phfmf atfmf msfmf imf rpp
## <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 T0 45 45 45 45 45 45 45 45 45
## 2 T1 45 45 45 45 45 45 45 45 45
## 3 T2 45 45 45 45 45 45 45 45 45
## 4 T3 45 45 45 45 45 45 45 45 45
## 5 T4 45 45 45 45 45 45 45 45 45
## 6 T5 45 45 45 45 45 45 45 45 45
## 7 T6 45 45 45 45 45 45 45 45 45
## 8 T7 45 45 45 45 45 45 45 45 45
## 9 T8 45 45 45 45 45 45 45 45 45
sm <- consumo %>%
group_by(treat) %>%
summarise(across(pdfmc:imf, ~ sum(!is.na(.))))
sm
## # A tibble: 9 × 8
## treat pdfmc ffmc cifmc ssfmc phfmc atfmc imf
## <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 T0 15 15 15 15 15 15 15
## 2 T1 15 15 15 15 15 15 15
## 3 T2 15 15 15 15 15 15 15
## 4 T3 15 15 15 15 15 15 15
## 5 T4 15 15 15 15 15 15 15
## 6 T5 15 15 15 15 15 15 15
## 7 T6 15 15 15 15 15 15 15
## 8 T7 15 15 15 15 15 15 15
## 9 T8 15 15 15 15 15 15 155 Objetivos
Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez fisiológica
Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez comercial
5.1 Objetivo Específico 1
Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez fisiológica
5.1.1 Firmeza de fruto (ffmf)
trait <- "ffmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol ffmf resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ffmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 29.954 14.9771 19.6376 0.0000000073854003 ***
## biol 2 48.734 24.3670 31.9495 0.0000000000001369 ***
## composts:biol 4 1.737 0.4344 0.5695 0.6849
## Residuals 396 302.018 0.7627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 12.22000 | 0.1301854 | 396 | 11.96406 | 12.47594 | a |
| 5 | 0 | 11.80444 | 0.1301854 | 396 | 11.54850 | 12.06039 | ab |
| 0 | 0 | 11.38667 | 0.1301854 | 396 | 11.13073 | 11.64261 | b |
| 10 | 5 | 12.20444 | 0.1301854 | 396 | 11.94850 | 12.46039 | a |
| 5 | 5 | 11.94667 | 0.1301854 | 396 | 11.69073 | 12.20261 | a |
| 0 | 5 | 11.49333 | 0.1301854 | 396 | 11.23739 | 11.74927 | b |
| 10 | 15 | 12.96000 | 0.1301854 | 396 | 12.70406 | 13.21594 | a |
| 5 | 15 | 12.33111 | 0.1301854 | 396 | 12.07517 | 12.58705 | b |
| 0 | 15 | 11.95556 | 0.1301854 | 396 | 11.69961 | 12.21150 | b |
p1a <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "compost"
, ylab = "Firmeza del fruto (Kgf)"
, ylimits = c(0, 16, 4)
)
p1a
5.2 Color interno del fruto (cifmf)
Excluir esta variable como cuantitativo
trait <- "cifmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol cifmf resi res_MAD
## 6 6 1 0 0 1.5 -0.6599549 -13.354004
## 9 9 1 0 0 2.5 0.3400451 6.880719
## 10 10 1 0 0 2.5 0.3400451 6.880719
## 11 11 1 0 0 2.5 0.3400451 6.880719
## 18 18 2 0 0 2.5 0.3400451 6.880719
## 21 21 2 0 0 2.5 0.3400451 6.880719
## 23 23 2 0 0 2.5 0.3400451 6.880719
## 24 24 2 0 0 2.5 0.3400451 6.880719
## 25 25 2 0 0 2.5 0.3400451 6.880719
## 26 26 2 0 0 2.5 0.3400451 6.880719
## 27 27 2 0 0 2.5 0.3400451 6.880719
## 28 28 2 0 0 2.5 0.3400451 6.880719
## 34 34 3 0 0 1.5 -0.6800902 -13.761438
## 36 36 3 0 0 3.0 0.8199098 16.590647
## 37 37 3 0 0 2.5 0.3199098 6.473285
## 38 38 3 0 0 2.5 0.3199098 6.473285
## 40 40 3 0 0 3.0 0.8199098 16.590647
## 52 52 1 5 0 2.5 0.3733785 7.555210
## 54 54 1 5 0 2.5 0.3733785 7.555210
## 55 55 1 5 0 2.5 0.3733785 7.555210
## 67 67 2 5 0 1.5 -0.6266215 -12.679513
## 68 68 2 5 0 2.5 0.3733785 7.555210
## 69 69 2 5 0 1.0 -1.1266215 -22.796875
## 70 70 2 5 0 2.5 0.3733785 7.555210
## 72 72 2 5 0 2.5 0.3733785 7.555210
## 76 76 3 5 0 2.5 0.3532431 7.147776
## 81 81 3 5 0 2.5 0.3532431 7.147776
## 83 83 3 5 0 2.5 0.3532431 7.147776
## 84 84 3 5 0 2.5 0.3532431 7.147776
## 85 85 3 5 0 2.5 0.3532431 7.147776
## 86 86 3 5 0 2.5 0.3532431 7.147776
## 87 87 3 5 0 2.5 0.3532431 7.147776
## 89 89 3 5 0 2.5 0.3532431 7.147776
## 90 90 3 5 0 2.5 0.3532431 7.147776
## 94 94 1 15 0 2.5 0.3733785 7.555210
## 96 96 1 15 0 2.5 0.3733785 7.555210
## 103 103 1 15 0 2.5 0.3733785 7.555210
## 104 104 1 15 0 2.5 0.3733785 7.555210
## 105 105 1 15 0 2.5 0.3733785 7.555210
## 108 108 2 15 0 2.5 0.3733785 7.555210
## 111 111 2 15 0 1.5 -0.6266215 -12.679513
## 116 116 2 15 0 2.5 0.3733785 7.555210
## 119 119 2 15 0 2.5 0.3733785 7.555210
## 124 124 3 15 0 2.5 0.3532431 7.147776
## 126 126 3 15 0 2.5 0.3532431 7.147776
## 129 129 3 15 0 2.5 0.3532431 7.147776
## 134 134 3 15 0 2.5 0.3532431 7.147776
## 135 135 3 15 0 2.5 0.3532431 7.147776
## 141 141 1 0 5 2.5 0.3400451 6.880719
## 142 142 1 0 5 2.5 0.3400451 6.880719
## 144 144 1 0 5 2.5 0.3400451 6.880719
## 146 146 1 0 5 2.5 0.3400451 6.880719
## 147 147 1 0 5 2.5 0.3400451 6.880719
## 156 156 2 0 5 2.5 0.3400451 6.880719
## 158 158 2 0 5 2.5 0.3400451 6.880719
## 160 160 2 0 5 2.5 0.3400451 6.880719
## 162 162 2 0 5 2.5 0.3400451 6.880719
## 163 163 2 0 5 2.5 0.3400451 6.880719
## 171 171 3 0 5 3.0 0.8199098 16.590647
## 175 175 3 0 5 2.5 0.3199098 6.473285
## 176 176 3 0 5 2.5 0.3199098 6.473285
## 177 177 3 0 5 2.5 0.3199098 6.473285
## 183 183 1 0 10 2.5 0.3733785 7.555210
## 185 185 1 0 10 2.5 0.3733785 7.555210
## 186 186 1 0 10 2.5 0.3733785 7.555210
## 189 189 1 0 10 2.5 0.3733785 7.555210
## 191 191 1 0 10 2.5 0.3733785 7.555210
## 192 192 1 0 10 2.5 0.3733785 7.555210
## 195 195 1 0 10 2.5 0.3733785 7.555210
## 204 204 2 0 10 2.5 0.3733785 7.555210
## 206 206 2 0 10 2.5 0.3733785 7.555210
## 208 208 2 0 10 1.5 -0.6266215 -12.679513
## 213 213 3 0 10 2.5 0.3532431 7.147776
## 223 223 3 0 10 3.0 0.8532431 17.265137
## 225 225 3 0 10 2.5 0.3532431 7.147776
## 231 231 1 5 5 2.5 0.3733785 7.555210
## 234 234 1 5 5 2.5 0.3733785 7.555210
## 235 235 1 5 5 2.5 0.3733785 7.555210
## 236 236 1 5 5 2.5 0.3733785 7.555210
## 243 243 2 5 5 2.5 0.3733785 7.555210
## 244 244 2 5 5 2.5 0.3733785 7.555210
## 246 246 2 5 5 2.5 0.3733785 7.555210
## 248 248 2 5 5 2.5 0.3733785 7.555210
## 250 250 2 5 5 2.5 0.3733785 7.555210
## 253 253 2 5 5 2.5 0.3733785 7.555210
## 255 255 2 5 5 2.5 0.3733785 7.555210
## 257 257 3 5 5 2.5 0.3532431 7.147776
## 269 269 3 5 5 1.5 -0.6467569 -13.086947
## 270 270 3 5 5 2.5 0.3532431 7.147776
## 281 281 1 5 10 2.5 0.3956007 8.004870
## 282 282 1 5 10 2.5 0.3956007 8.004870
## 285 285 1 5 10 2.5 0.3956007 8.004870
## 296 296 2 5 10 2.5 0.3956007 8.004870
## 298 298 2 5 10 2.5 0.3956007 8.004870
## 300 300 2 5 10 2.5 0.3956007 8.004870
## 301 301 3 5 10 2.5 0.3754653 7.597437
## 305 305 3 5 10 2.5 0.3754653 7.597437
## 310 310 3 5 10 2.5 0.3754653 7.597437
## 311 311 3 5 10 2.5 0.3754653 7.597437
## 316 316 1 15 5 2.5 0.3511562 7.105549
## 321 321 1 15 5 2.5 0.3511562 7.105549
## 325 325 1 15 5 2.5 0.3511562 7.105549
## 329 329 1 15 5 2.5 0.3511562 7.105549
## 332 332 2 15 5 2.5 0.3511562 7.105549
## 336 336 2 15 5 2.5 0.3511562 7.105549
## 338 338 2 15 5 2.5 0.3511562 7.105549
## 342 342 2 15 5 2.5 0.3511562 7.105549
## 346 346 3 15 5 3.0 0.8310209 16.815477
## 348 348 3 15 5 3.0 0.8310209 16.815477
## 354 354 3 15 5 2.5 0.3310209 6.698116
## 358 358 3 15 5 2.5 0.3310209 6.698116
## 362 362 1 15 10 2.0 -0.4821771 -9.756720
## 368 368 1 15 10 2.0 -0.4821771 -9.756720
## 374 374 1 15 10 2.0 -0.4821771 -9.756720
## 375 375 1 15 10 2.0 -0.4821771 -9.756720
## 379 379 2 15 10 3.0 0.5178229 10.478003
## 387 387 2 15 10 2.0 -0.4821771 -9.756720
## 388 388 2 15 10 2.0 -0.4821771 -9.756720
## 391 391 3 15 10 3.0 0.4976875 10.070569
## 393 393 3 15 10 3.0 0.4976875 10.070569
## 395 395 3 15 10 3.0 0.4976875 10.070569
## 398 398 3 15 10 3.0 0.4976875 10.070569
## 399 399 3 15 10 2.0 -0.5023125 -10.164153
## 402 402 3 15 10 2.0 -0.5023125 -10.164153
## 403 403 3 15 10 3.0 0.4976875 10.070569
## 405 405 3 15 10 3.0 0.4976875 10.070569
## rawp.BHStud adjp bholm
## 6 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 9 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 10 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 11 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 18 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 21 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 23 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 24 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 25 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 26 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 27 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 28 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 34 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 36 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 37 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 38 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 40 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 52 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 54 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 55 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 67 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 68 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 69 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 70 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 72 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 76 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 81 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 83 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 84 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 85 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 86 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 87 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 89 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 90 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 94 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 96 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 103 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 104 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 105 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 108 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 111 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 116 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 119 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 124 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 126 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 129 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 134 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 135 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 141 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 142 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 144 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 146 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 147 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 156 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 158 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 160 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 162 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 163 0.000000000005955236304 0.000000000005955236304 0.0000000018282575454
## 171 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 175 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 176 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 177 0.000000000095894625574 0.000000000095894625574 0.0000000272340736629
## 183 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 185 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 186 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 189 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 191 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 192 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 195 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 204 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 206 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 208 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 213 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 223 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 225 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 231 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 234 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 235 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 236 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 243 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 244 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 246 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 248 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 250 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 253 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 255 0.000000000000041744386 0.000000000000041744386 0.0000000000153201896
## 257 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 269 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 270 0.000000000000881961171 0.000000000000881961171 0.0000000002936930699
## 281 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 282 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 285 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 296 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 298 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 300 0.000000000000001110223 0.000000000000001110223 0.0000000000004185541
## 301 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 305 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 310 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 311 0.000000000000030198066 0.000000000000030198066 0.0000000000112034826
## 316 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 321 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 325 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 329 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 332 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 336 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 338 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 342 0.000000000001198374733 0.000000000001198374733 0.0000000003774880408
## 346 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 348 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 354 0.000000000021112445125 0.000000000021112445125 0.0000000060381593059
## 358 0.000000000021112445125 0.000000000021112445125 0.0000000060381593059
## 362 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 368 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 374 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 375 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 379 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 387 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 388 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 391 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 393 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 395 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 398 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 399 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 402 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 403 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## 405 0.000000000000000000000 0.000000000000000000000 0.0000000000000000000
## out_flag
## 6 OUTLIER
## 9 OUTLIER
## 10 OUTLIER
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## 18 OUTLIER
## 21 OUTLIER
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## 255 OUTLIER
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## 269 OUTLIER
## 270 OUTLIER
## 281 OUTLIER
## 282 OUTLIER
## 285 OUTLIER
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## 321 OUTLIER
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## 342 OUTLIER
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## 354 OUTLIER
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## 362 OUTLIER
## 368 OUTLIER
## 374 OUTLIER
## 375 OUTLIER
## 379 OUTLIER
## 387 OUTLIER
## 388 OUTLIER
## 391 OUTLIER
## 393 OUTLIER
## 395 OUTLIER
## 398 OUTLIER
## 399 OUTLIER
## 402 OUTLIER
## 403 OUTLIER
## 405 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: cifmf
## Df Sum Sq Mean Sq F value
## composts 2 1.5872 0.79358 13798771145988843021802240660
## biol 2 1.6520 0.82599 14362199539619202704044242464
## composts:biol 4 3.4544 0.86360 15016274787197207858626608666
## Residuals 270 0.0000 0.00000
## Pr(>F)
## composts < 0.00000000000000022 ***
## biol < 0.00000000000000022 ***
## composts:biol < 0.00000000000000022 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | 5 | 0 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 3 | 10 | 0 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 2 | 0 | 0 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 4 | 10 | 5 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 5 | 5 | 5 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 6 | 0 | 5 | 2.0 | 0 | 270 | 2.0 | 2.0 | a |
| 7 | 10 | 15 | 2.5 | 0 | 270 | 2.5 | 2.5 | a |
| 8 | 5 | 15 | 2.0 | 0 | 270 | 2.0 | 2.0 | b |
| 9 | 0 | 15 | 2.0 | 0 | 270 | 2.0 | 2.0 | b |
p1b <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Color interno del fruto"
, ylimits = c(0, 3, 1)
)
p1b
5.3 Potencia de hidrogeno del fruto (phfmf)
trait <- "phfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol phfmf resi res_MAD rawp.BHStud
## 390 390 2 15 10 3.507795 0.7353291 3.98034 0.0000688167
## adjp bholm out_flag
## 390 0.0000688167 0.02787076 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: phfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 0.8058 0.40290 12.1492 0.00000757832 ***
## biol 2 1.2013 0.60065 18.1126 0.00000002978 ***
## composts:biol 4 0.5173 0.12932 3.8996 0.004051 **
## Residuals 395 13.0991 0.03316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | 10 | 0 | 2.711098 | 0.0271466 | 395 | 2.657728 | 2.764468 | a |
| 1 | 0 | 0 | 2.594716 | 0.0271466 | 395 | 2.541346 | 2.648086 | b |
| 3 | 5 | 0 | 2.526739 | 0.0271466 | 395 | 2.473369 | 2.580109 | b |
| 4 | 10 | 5 | 2.737604 | 0.0271466 | 395 | 2.684234 | 2.790974 | a |
| 5 | 5 | 5 | 2.634018 | 0.0271466 | 395 | 2.580648 | 2.687388 | b |
| 6 | 0 | 5 | 2.540791 | 0.0271466 | 395 | 2.487421 | 2.594161 | c |
| 7 | 10 | 15 | 2.745579 | 0.0274534 | 395 | 2.691606 | 2.799552 | a |
| 8 | 5 | 15 | 2.710735 | 0.0271466 | 395 | 2.657365 | 2.764104 | a |
| 9 | 0 | 15 | 2.692818 | 0.0271466 | 395 | 2.639448 | 2.746188 | a |
p1c <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "pH del Fruto"
, ylimits = c(0, 4, 1)
)
p1c
5.4 Solidos solubles del fruto (ssfmf)
trait <- "ssfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol ssfmf resi res_MAD rawp.BHStud
## 390 390 2 15 10 12.1 2.787154 4.673809 0.000002956642
## adjp bholm out_flag
## 390 0.000002956642 0.00119744 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ssfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 10.124 5.0620 13.9979 0.000001338318 ***
## biol 2 14.392 7.1959 19.8987 0.000000005838 ***
## composts:biol 4 4.753 1.1882 3.2858 0.01146 *
## Residuals 395 142.842 0.3616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | 10 | 0 | 9.194444 | 0.0896445 | 395 | 9.018204 | 9.370684 | a |
| 2 | 5 | 0 | 8.767778 | 0.0896445 | 395 | 8.591538 | 8.944018 | b |
| 3 | 0 | 0 | 8.574444 | 0.0896445 | 395 | 8.398205 | 8.750684 | b |
| 4 | 10 | 5 | 9.291111 | 0.0896445 | 395 | 9.114871 | 9.467351 | a |
| 5 | 5 | 5 | 8.913333 | 0.0896445 | 395 | 8.737093 | 9.089573 | b |
| 6 | 0 | 5 | 8.646667 | 0.0896445 | 395 | 8.470427 | 8.822907 | b |
| 7 | 5 | 15 | 9.293333 | 0.0896445 | 395 | 9.117093 | 9.469573 | a |
| 9 | 10 | 15 | 9.244318 | 0.0906575 | 395 | 9.066087 | 9.422550 | a |
| 8 | 0 | 15 | 9.127778 | 0.0896445 | 395 | 8.951538 | 9.304018 | a |
p1d <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Solidos solubles del fruto (brix^{o})"
, ylimits = c(0, 12, 2)
)
p1d
5.5 Acidez titulable del fruto (atfmf)
trait <- "atfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol atfmf resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: atfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 0.19915 0.099574 15.5979 0.00000030165 ***
## biol 2 0.23570 0.117851 18.4609 0.00000002161 ***
## composts:biol 4 0.08017 0.020043 3.1397 0.01465 *
## Residuals 396 2.52799 0.006384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 3 | 0 | 0 | 1.365333 | 0.0119106 | 396 | 1.341917 | 1.388749 | a |
| 2 | 5 | 0 | 1.313556 | 0.0119106 | 396 | 1.290140 | 1.336971 | b |
| 1 | 10 | 0 | 1.260444 | 0.0119106 | 396 | 1.237029 | 1.283860 | c |
| 6 | 5 | 5 | 1.301556 | 0.0119106 | 396 | 1.278140 | 1.324971 | a |
| 4 | 0 | 5 | 1.298000 | 0.0119106 | 396 | 1.274584 | 1.321416 | a |
| 5 | 10 | 5 | 1.270889 | 0.0119106 | 396 | 1.247473 | 1.294305 | a |
| 9 | 0 | 15 | 1.278000 | 0.0119106 | 396 | 1.254584 | 1.301416 | a |
| 8 | 5 | 15 | 1.263889 | 0.0119106 | 396 | 1.240473 | 1.287305 | ab |
| 7 | 10 | 15 | 1.235111 | 0.0119106 | 396 | 1.211695 | 1.258527 | b |
p1e <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "compost"
, ylab = "Acidez titulable del fruto ('%')"
, ylimits = c(0, 2, 1)
)
p1e
5.6 Materia seca del fruto (msfmf)
trait <- "msfmf"
fb <- fisio
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol msfmf resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: msfmf
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 45.336 22.668 30.4208 0.0000000000005129 ***
## biol 2 72.171 36.085 48.4270 < 0.00000000000000022 ***
## composts:biol 4 6.782 1.695 2.2753 0.06058 .
## Residuals 396 295.079 0.745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 20.02778 | 0.1286813 | 396 | 19.77479 | 20.28076 | a |
| 5 | 0 | 19.31778 | 0.1286813 | 396 | 19.06479 | 19.57076 | b |
| 0 | 0 | 19.26889 | 0.1286813 | 396 | 19.01590 | 19.52187 | b |
| 10 | 5 | 20.00222 | 0.1286813 | 396 | 19.74924 | 20.25521 | a |
| 5 | 5 | 19.55744 | 0.1286813 | 396 | 19.30446 | 19.81043 | b |
| 0 | 5 | 19.07333 | 0.1286813 | 396 | 18.82035 | 19.32632 | c |
| 10 | 15 | 20.96133 | 0.1286813 | 396 | 20.70835 | 21.21432 | a |
| 5 | 15 | 20.21600 | 0.1286813 | 396 | 19.96302 | 20.46898 | b |
| 0 | 15 | 19.57556 | 0.1286813 | 396 | 19.32257 | 19.82854 | c |
p1f <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "compost"
, ylab = "Materia seca del fruto ('%')"
, ylimits = c(0, 25, 5)
)
p1f
5.7 Figure 1
legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)
p1 <- list(p1a + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
# , p1b + labs(x = NULL) + theme(legend.position="none"
# , axis.title.x=element_blank()
# , axis.text.x=element_blank()
# , axis.ticks.x=element_blank())
# , p1c + labs(x = NULL) + theme(legend.position="none"
# , axis.title.x=element_blank()
# , axis.text.x=element_blank()
# , axis.ticks.x=element_blank())
, p1d + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1e + theme(legend.position="none")
, p1f + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 2
, labels = "auto"
)
plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1)) %>%
ggsave2(plot = ., "files/Fig-1.jpg"
, units = "cm"
, width = 20
, height = 18
)
knitr::include_graphics("files/Fig-1.jpg")
5.8 Multivariate
mv <- fisio %>%
group_by(composts, biol) %>%
summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
unite("treat", composts:biol, sep = "-") %>%
rename(Treat = treat)
pca <- mv %>%
PCA(scale.unit = T, quali.sup = 1, graph = F)
# summary
summary(pca, nbelements = Inf, nb.dec = 2)
##
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8
## Variance 7.14 0.92 0.52 0.30 0.10 0.02 0.01 0.00
## % of var. 79.29 10.27 5.74 3.28 1.11 0.21 0.10 0.01
## Cumulative % of var. 79.29 89.56 95.30 98.58 99.69 99.89 99.99 100.00
##
## Individuals
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## 1 | 4.39 | -4.14 26.70 0.89 | 0.91 9.94 0.04 | -0.02 0.01 0.00 |
## 2 | 2.85 | -2.46 9.45 0.75 | 0.60 4.36 0.04 | -0.94 18.96 0.11 |
## 3 | 1.94 | 1.03 1.66 0.28 | -0.67 5.42 0.12 | -1.41 42.81 0.53 |
## 4 | 3.09 | -2.82 12.40 0.83 | 0.06 0.04 0.00 | 0.87 16.47 0.08 |
## 5 | 0.98 | -0.79 0.98 0.66 | -0.20 0.47 0.04 | 0.32 2.18 0.11 |
## 6 | 2.04 | 1.64 4.19 0.65 | -1.13 15.46 0.31 | -0.20 0.89 0.01 |
## 7 | 1.35 | 0.50 0.38 0.14 | -0.88 9.25 0.42 | 0.85 15.42 0.39 |
## 8 | 2.36 | 2.19 7.47 0.86 | -0.71 6.07 0.09 | 0.34 2.43 0.02 |
## 9 | 5.27 | 4.86 36.77 0.85 | 2.02 48.98 0.15 | 0.20 0.83 0.00 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## pcfmf | 0.96 12.91 0.92 | -0.04 0.18 0.00 | -0.11 2.41 0.01 |
## ffmf | 0.97 13.27 0.95 | 0.17 3.23 0.03 | -0.12 2.91 0.02 |
## cifmf | 0.57 4.59 0.33 | 0.81 71.60 0.66 | 0.07 0.95 0.00 |
## ssfmf | 0.95 12.55 0.90 | -0.29 9.06 0.08 | -0.10 1.89 0.01 |
## phfmf | 0.90 11.35 0.81 | -0.17 3.25 0.03 | -0.02 0.06 0.00 |
## atfmf | -0.93 12.00 0.86 | 0.13 1.88 0.02 | 0.01 0.03 0.00 |
## msfmf | 0.95 12.62 0.90 | 0.23 5.63 0.05 | -0.12 2.84 0.01 |
## imf | 0.97 13.20 0.94 | -0.20 4.29 0.04 | -0.07 1.05 0.01 |
## rpp | 0.73 7.51 0.54 | -0.09 0.88 0.01 | 0.67 87.86 0.45 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3 cos2 v.test
## 0-0 | 4.39 | -4.14 0.89 -1.55 | 0.91 0.04 0.95 | -0.02 0.00 -0.03 |
## 0-10 | 1.94 | 1.03 0.28 0.39 | -0.67 0.12 -0.70 | -1.41 0.53 -1.96 |
## 0-5 | 2.85 | -2.46 0.75 -0.92 | 0.60 0.04 0.63 | -0.94 0.11 -1.31 |
## 15-0 | 1.35 | 0.50 0.14 0.19 | -0.88 0.42 -0.91 | 0.85 0.39 1.18 |
## 15-10 | 5.27 | 4.86 0.85 1.82 | 2.02 0.15 2.10 | 0.20 0.00 0.27 |
## 15-5 | 2.36 | 2.19 0.86 0.82 | -0.71 0.09 -0.74 | 0.34 0.02 0.47 |
## 5-0 | 3.09 | -2.82 0.83 -1.06 | 0.06 0.00 0.06 | 0.87 0.08 1.22 |
## 5-10 | 2.04 | 1.64 0.65 0.61 | -1.13 0.31 -1.18 | -0.20 0.01 -0.28 |
## 5-5 | 0.98 | -0.79 0.66 -0.30 | -0.20 0.04 -0.21 | 0.32 0.11 0.44 |
f2a <- plot.PCA(x = pca, choix = "var"
, cex=0.8
)
f2b <- plot.PCA(x = pca, choix = "ind"
, habillage = 1
, invisible = c("ind")
, cex=0.8
, ylim = c(-3,3)
) 5.9 Figure 2
list(f2a, f2b) %>%
plot_grid(plotlist = ., ncol = 2, nrow = 1
, labels = "auto"
, rel_widths = c(1, 1.5)
) %>%
ggsave2(plot = ., "files/Fig-2.jpg", units = "cm"
, width = 25
, height = 10
)
knitr::include_graphics("files/Fig-2.jpg")
6 Correlation
cor <- mv %>%
dplyr::select(where(is.numeric)) %>%
cor(., method ="pearson")
cor %>% kable()| pcfmf | ffmf | cifmf | ssfmf | phfmf | atfmf | msfmf | imf | rpp | |
|---|---|---|---|---|---|---|---|---|---|
| pcfmf | 1.0000000 | 0.9608678 | 0.4994040 | 0.9307446 | 0.8014322 | -0.9032846 | 0.8923145 | 0.9432362 | 0.6402353 |
| ffmf | 0.9608678 | 1.0000000 | 0.6839396 | 0.8811729 | 0.8263723 | -0.8804054 | 0.9730600 | 0.9158768 | 0.6176584 |
| cifmf | 0.4994040 | 0.6839396 | 1.0000000 | 0.2991542 | 0.3781309 | -0.4311611 | 0.7140435 | 0.3932697 | 0.3897352 |
| ssfmf | 0.9307446 | 0.8811729 | 0.2991542 | 1.0000000 | 0.9232590 | -0.8857042 | 0.8519632 | 0.9763968 | 0.6542042 |
| phfmf | 0.8014322 | 0.8263723 | 0.3781309 | 0.9232590 | 1.0000000 | -0.7551839 | 0.8659197 | 0.8841685 | 0.6578585 |
| atfmf | -0.9032846 | -0.8804054 | -0.4311611 | -0.8857042 | -0.7551839 | 1.0000000 | -0.8043641 | -0.9630024 | -0.6729750 |
| msfmf | 0.8923145 | 0.9730600 | 0.7140435 | 0.8519632 | 0.8659197 | -0.8043641 | 1.0000000 | 0.8719723 | 0.5948866 |
| imf | 0.9432362 | 0.9158768 | 0.3932697 | 0.9763968 | 0.8841685 | -0.9630024 | 0.8719723 | 1.0000000 | 0.6752525 |
| rpp | 0.6402353 | 0.6176584 | 0.3897352 | 0.6542042 | 0.6578585 | -0.6729750 | 0.5948866 | 0.6752525 | 1.0000000 |
sf1 <- ~ {
corrplot::corrplot(cor, method = "number", type = "upper")
}
list(sf1) %>%
plot_grid(plotlist = .) %>%
ggsave2(plot = ., "files/FigS1.jpg", units = "cm"
, width = 15, height = 15)
knitr::include_graphics("files/FigS1.jpg")
6.0.1 Objetivo Específico 2
Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez comercial
7 Porcentaje deshidratación del fruto (pdfmc)
trait <- "pdfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol pdfmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: pdfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 2.9454 1.47269 28.9546 0.00000000004501 ***
## biol 2 1.6263 0.81316 15.9875 0.00000064906170 ***
## composts:biol 4 0.2066 0.05164 1.0153 0.4022
## Residuals 126 6.4086 0.05086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 3 | 0 | 0 | 6.760667 | 0.0582307 | 126 | 6.645430 | 6.875903 | a |
| 2 | 5 | 0 | 6.539333 | 0.0582307 | 126 | 6.424097 | 6.654570 | b |
| 1 | 10 | 0 | 6.408000 | 0.0582307 | 126 | 6.292763 | 6.523237 | b |
| 6 | 0 | 5 | 6.685333 | 0.0582307 | 126 | 6.570097 | 6.800570 | a |
| 5 | 5 | 5 | 6.492000 | 0.0582307 | 126 | 6.376763 | 6.607237 | ab |
| 4 | 10 | 5 | 6.375333 | 0.0582307 | 126 | 6.260097 | 6.490570 | b |
| 9 | 0 | 15 | 6.301333 | 0.0582307 | 126 | 6.186096 | 6.416570 | a |
| 8 | 5 | 15 | 6.236000 | 0.0582307 | 126 | 6.120763 | 6.351237 | a |
| 7 | 10 | 15 | 6.162667 | 0.0582307 | 126 | 6.047430 | 6.277903 | a |
p2a <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Deshidratación del fruto ('%')"
, ylimits = c(0, 8, 2)
)
p2a
8 Solidos solubles del fruto (ssfmc)
trait <- "ssfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol ssfmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ssfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 13.192 6.5961 19.7094 0.00000003569 ***
## biol 2 6.876 3.4379 10.2725 0.00007365188 ***
## composts:biol 4 0.332 0.0830 0.2479 0.9105
## Residuals 126 42.168 0.3347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 15.44000 | 0.149369 | 126 | 15.14440 | 15.73560 | a |
| 5 | 0 | 15.04667 | 0.149369 | 126 | 14.75107 | 15.34226 | ab |
| 0 | 0 | 14.92667 | 0.149369 | 126 | 14.63107 | 15.22226 | b |
| 10 | 5 | 15.46667 | 0.149369 | 126 | 15.17107 | 15.76226 | a |
| 5 | 5 | 15.22000 | 0.149369 | 126 | 14.92440 | 15.51560 | ab |
| 0 | 5 | 14.84667 | 0.149369 | 126 | 14.55107 | 15.14226 | b |
| 10 | 15 | 16.12000 | 0.149369 | 126 | 15.82440 | 16.41560 | a |
| 5 | 15 | 15.72667 | 0.149369 | 126 | 15.43107 | 16.02226 | ab |
| 0 | 15 | 15.61333 | 0.149369 | 126 | 15.31774 | 15.90893 | b |
p2b <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Solidos solubles del fruto (brix^{o})"
, ylimits = c(0, 18, 3)
)
p2b
9 Acidez titulable del fruto (atfmc)
trait <- "atfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol atfmc resi res_MAD rawp.BHStud
## 4 4 1 0 0 0.50 -0.1157152 -4.533406 0.00000580400
## 5 5 1 0 0 0.50 -0.1157152 -4.533406 0.00000580400
## 18 18 1 5 0 0.74 0.1062848 4.163951 0.00003127878
## 27 27 3 5 0 0.50 -0.1006342 -3.942576 0.00008061124
## adjp bholm out_flag
## 4 0.00000580400 0.0007835399 OUTLIER
## 5 0.00000580400 0.0007835399 OUTLIER
## 18 0.00003127878 0.0041600772 OUTLIER
## 27 0.00008061124 0.0106406836 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: atfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 0.219750 0.109875 94.7899 < 0.00000000000000022 ***
## biol 2 0.054531 0.027266 23.5222 0.00000000229 ***
## composts:biol 4 0.023460 0.005865 5.0598 0.0008311 ***
## Residuals 122 0.141416 0.001159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | 0 | 0 | 0.6169231 | 0.0094427 | 122 | 0.5982303 | 0.6356159 | a |
| 3 | 10 | 0 | 0.5946667 | 0.0087907 | 122 | 0.5772646 | 0.6120687 | a |
| 1 | 5 | 0 | 0.5940000 | 0.0087907 | 122 | 0.5765979 | 0.6114021 | a |
| 6 | 0 | 5 | 0.6192308 | 0.0094427 | 122 | 0.6005380 | 0.6379236 | a |
| 5 | 5 | 5 | 0.5496667 | 0.0087907 | 122 | 0.5322646 | 0.5670687 | b |
| 4 | 10 | 5 | 0.5346667 | 0.0087907 | 122 | 0.5172646 | 0.5520687 | b |
| 9 | 0 | 15 | 0.5193333 | 0.0087907 | 122 | 0.5019313 | 0.5367354 | a |
| 8 | 5 | 15 | 0.5138667 | 0.0087907 | 122 | 0.4964646 | 0.5312687 | a |
| 7 | 10 | 15 | 0.4746667 | 0.0087907 | 122 | 0.4572646 | 0.4920687 | b |
p2c <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Acidez titulable del fruto ('%')"
, ylimits = c(0, 0.8, 0.2)
)
p2c
10 Potencial de hidrógeno del fruto (phfmc)
trait <- "phfmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## index repetition composts biol phfmc resi res_MAD rawp.BHStud
## 27 27 3 5 0 3.8 -0.4029042 -4.421673 0.00000979398
## adjp bholm out_flag
## 27 0.00000979398 0.001322187 OUTLIER
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: phfmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 1.51062 0.75531 54.8131 < 0.00000000000000022 ***
## biol 2 0.64152 0.32076 23.2776 0.00000000255 ***
## composts:biol 4 0.26493 0.06623 4.8065 0.001221 **
## Residuals 125 1.72246 0.01378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 4.388000 | 0.0303092 | 125 | 4.328014 | 4.447986 | a |
| 5 | 0 | 4.369333 | 0.0303092 | 125 | 4.309348 | 4.429319 | a |
| 0 | 0 | 4.332000 | 0.0303092 | 125 | 4.272014 | 4.391986 | a |
| 10 | 5 | 4.484000 | 0.0303092 | 125 | 4.424014 | 4.543986 | a |
| 5 | 5 | 4.429333 | 0.0303092 | 125 | 4.369348 | 4.489319 | a |
| 0 | 5 | 4.201429 | 0.0313730 | 125 | 4.139337 | 4.263520 | b |
| 10 | 15 | 4.692667 | 0.0303092 | 125 | 4.632681 | 4.752652 | a |
| 5 | 15 | 4.568667 | 0.0303092 | 125 | 4.508681 | 4.628652 | b |
| 0 | 15 | 4.520000 | 0.0303092 | 125 | 4.460014 | 4.579986 | b |
p2d <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, ylab = "pH del Fruto"
, ylimits = c(0, 6, 2)
)
p2d
11 Color interno del fruto (cifmc)
trait <- "cifmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol cifmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: cifmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 1.4890 0.74452 5.4384 0.005427 **
## biol 2 1.9690 0.98452 7.1915 0.001103 **
## composts:biol 4 2.0914 0.52285 3.8192 0.005776 **
## Residuals 126 17.2493 0.13690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group |
|---|---|---|---|---|---|---|---|
| 10 | 0 | 3.766667 | 0.0955334 | 126 | 3.577609 | 3.955725 | a |
| 5 | 0 | 3.566667 | 0.0955334 | 126 | 3.377609 | 3.755724 | a |
| 0 | 0 | 3.066667 | 0.0955334 | 126 | 2.877609 | 3.255724 | b |
| 10 | 5 | 3.633333 | 0.0955334 | 126 | 3.444275 | 3.822391 | a |
| 5 | 5 | 3.633333 | 0.0955334 | 126 | 3.444275 | 3.822391 | a |
| 0 | 5 | 3.593333 | 0.0955334 | 126 | 3.404275 | 3.782391 | a |
| 10 | 15 | 3.800000 | 0.0955334 | 126 | 3.610942 | 3.989058 | a |
| 5 | 15 | 3.700000 | 0.0955334 | 126 | 3.510942 | 3.889058 | a |
| 0 | 15 | 3.666667 | 0.0955334 | 126 | 3.477609 | 3.855724 | a |
p2e <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "compost"
, ylab = "Color interno del fruto"
, ylimits = c(0, 5, 1)
)
p2e
12 Firmeza del fruto (ffmc)
trait <- "ffmc"
cs <- consumo
lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()
lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()
rmout <- cs %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers
## [1] index repetition composts biol ffmc resi
## [7] res_MAD rawp.BHStud adjp bholm out_flag
## <0 rows> (o 0- extensión row.names)
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ffmc
## Df Sum Sq Mean Sq F value Pr(>F)
## composts 2 6.6441 3.3221 25.4488 0.000000000521 ***
## biol 2 2.7739 1.3870 10.6248 0.000054440296 ***
## composts:biol 4 1.4536 0.3634 2.7839 0.02947 *
## Residuals 126 16.4480 0.1305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ biol|composts) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| biol | composts | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | 10 | 0 | 4.053333 | 0.093288 | 126 | 3.868719 | 4.237947 | a |
| 1 | 0 | 0 | 3.546667 | 0.093288 | 126 | 3.362052 | 3.731281 | b |
| 3 | 5 | 0 | 3.440000 | 0.093288 | 126 | 3.255386 | 3.624614 | b |
| 4 | 10 | 5 | 4.093333 | 0.093288 | 126 | 3.908719 | 4.277947 | a |
| 5 | 5 | 5 | 4.040000 | 0.093288 | 126 | 3.855386 | 4.224614 | a |
| 6 | 0 | 5 | 3.813333 | 0.093288 | 126 | 3.628719 | 3.997947 | a |
| 7 | 10 | 15 | 4.333333 | 0.093288 | 126 | 4.148719 | 4.517948 | a |
| 8 | 5 | 15 | 4.213333 | 0.093288 | 126 | 4.028719 | 4.397947 | a |
| 9 | 0 | 15 | 4.120000 | 0.093288 | 126 | 3.935386 | 4.304614 | a |
p2f <- mc %>%
plot_smr(x = "composts"
, y = "emmean"
, group = "biol"
, sig = "group"
, error = "SE"
, color = T
, xlab = "compost"
, ylab = "Firmeza del fruto (Kgf)"
, ylimits = c(0, 6, 2)
)
p2f
12.1 Figure 3
legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)
p2 <- list(p2a + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p2b + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p2c + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p2d + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p2e + theme(legend.position="none")
, p2f + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 2
, labels = "auto"
)
plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1)) %>%
ggsave2(plot = ., "files/Fig-3.jpg"
, units = "cm"
, width = 26
, height = 24
)
knitr::include_graphics("files/Fig-3.jpg")
12.2 Multivariate
mv <- consumo %>%
group_by(composts, biol) %>%
summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
unite("treat", composts:biol, sep = "-") %>%
rename(Treat = treat)
pca <- mv %>%
PCA(scale.unit = T, quali.sup = 1, graph = F)
# summary
summary(pca, nbelements = Inf, nb.dec = 2)
##
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 5.94 0.72 0.22 0.08 0.03 0.01 0.00
## % of var. 84.79 10.35 3.12 1.10 0.46 0.18 0.01
## Cumulative % of var. 84.79 95.14 98.26 99.36 99.81 99.99 100.00
##
## Individuals
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## 1 | 3.99 | -3.53 23.36 0.78 | -1.82 50.71 0.21 | 0.30 4.70 0.01 |
## 2 | 2.48 | -2.15 8.62 0.75 | 0.02 0.01 0.00 | -1.22 76.40 0.24 |
## 3 | 1.45 | -0.25 0.11 0.03 | 1.28 25.17 0.78 | -0.03 0.05 0.00 |
## 4 | 3.26 | -3.01 16.91 0.85 | 1.12 19.22 0.12 | 0.47 11.33 0.02 |
## 5 | 0.61 | -0.15 0.04 0.06 | 0.20 0.60 0.10 | 0.33 5.43 0.28 |
## 6 | 0.89 | 0.85 1.36 0.92 | -0.05 0.04 0.00 | 0.18 1.59 0.04 |
## 7 | 1.64 | 1.62 4.92 0.98 | -0.15 0.35 0.01 | 0.02 0.03 0.00 |
## 8 | 2.31 | 2.29 9.85 0.99 | -0.11 0.19 0.00 | 0.04 0.08 0.00 |
## 9 | 4.36 | 4.31 34.81 0.98 | -0.49 3.71 0.01 | -0.09 0.39 0.00 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr cos2
## pdfmc | -0.98 16.06 0.95 | -0.11 1.57 0.01 | 0.14 9.00 0.02 |
## ffmc | 0.89 13.39 0.79 | 0.22 6.42 0.05 | 0.39 71.20 0.16 |
## cifmc | 0.71 8.50 0.50 | 0.68 64.34 0.47 | -0.15 9.80 0.02 |
## ssfmc | 0.98 16.24 0.96 | -0.06 0.49 0.00 | -0.04 0.73 0.00 |
## phfmc | 0.94 14.82 0.88 | -0.30 12.42 0.09 | -0.14 8.48 0.02 |
## atfmc | -0.95 15.29 0.91 | 0.25 8.69 0.06 | -0.03 0.35 0.00 |
## imf | 0.97 15.70 0.93 | -0.21 6.07 0.04 | 0.03 0.44 0.00 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3 cos2 v.test
## 0-0 | 3.99 | -3.53 0.78 -1.45 | -1.82 0.21 -2.14 | 0.30 0.01 0.65 |
## 0-10 | 1.45 | -0.25 0.03 -0.10 | 1.28 0.78 1.51 | -0.03 0.00 -0.07 |
## 0-5 | 2.48 | -2.15 0.75 -0.88 | 0.02 0.00 0.03 | -1.22 0.24 -2.62 |
## 15-0 | 1.64 | 1.62 0.98 0.67 | -0.15 0.01 -0.18 | 0.02 0.00 0.05 |
## 15-10 | 4.36 | 4.31 0.98 1.77 | -0.49 0.01 -0.58 | -0.09 0.00 -0.19 |
## 15-5 | 2.31 | 2.29 0.99 0.94 | -0.11 0.00 -0.13 | 0.04 0.00 0.08 |
## 5-0 | 3.26 | -3.01 0.85 -1.23 | 1.12 0.12 1.32 | 0.47 0.02 1.01 |
## 5-10 | 0.89 | 0.85 0.92 0.35 | -0.05 0.00 -0.06 | 0.18 0.04 0.38 |
## 5-5 | 0.61 | -0.15 0.06 -0.06 | 0.20 0.10 0.23 | 0.33 0.28 0.70 |
f4a <- plot.PCA(x = pca, choix = "var"
, cex=0.8
)
f4b <- plot.PCA(x = pca, choix = "ind"
, habillage = 1
, invisible = c("ind")
, cex=0.8
, ylim = c(-3,3)
) 12.3 Figure 4
list(f4a, f4b) %>%
plot_grid(plotlist = ., ncol = 2, nrow = 1
, labels = "auto"
, rel_widths = c(1, 1.5)
) %>%
ggsave2(plot = ., "files/Fig-4.jpg", units = "cm"
, width = 25
, height = 10
)
knitr::include_graphics("files/Fig-4.jpg")
13 Correlation
cor <- mv %>%
dplyr::select(where(is.numeric)) %>%
cor(., method ="pearson")
cor %>% kable()| pdfmc | ffmc | cifmc | ssfmc | phfmc | atfmc | imf | |
|---|---|---|---|---|---|---|---|
| pdfmc | 1.0000000 | -0.8429407 | -0.7772793 | -0.9624332 | -0.9051190 | 0.8929691 | -0.9012038 |
| ffmc | -0.8429407 | 1.0000000 | 0.7184836 | 0.8508419 | 0.7231591 | -0.7997702 | 0.8182849 |
| cifmc | -0.7772793 | 0.7184836 | 1.0000000 | 0.6516900 | 0.4781254 | -0.5128373 | 0.5481724 |
| ssfmc | -0.9624332 | 0.8508419 | 0.6516900 | 1.0000000 | 0.9468725 | -0.9204049 | 0.9520934 |
| phfmc | -0.9051190 | 0.7231591 | 0.4781254 | 0.9468725 | 1.0000000 | -0.9583658 | 0.9515053 |
| atfmc | 0.8929691 | -0.7997702 | -0.5128373 | -0.9204049 | -0.9583658 | 1.0000000 | -0.9846427 |
| imf | -0.9012038 | 0.8182849 | 0.5481724 | 0.9520934 | 0.9515053 | -0.9846427 | 1.0000000 |
sf2 <- ~ {
corrplot::corrplot(cor, method = "number", type = "upper")
}
list(sf2) %>%
plot_grid(plotlist = .) %>%
ggsave2(plot = ., "files/FigS2.jpg", units = "cm"
, width = 15, height = 15)
knitr::include_graphics("files/FigS2.jpg")
14 Datos metereológicos
met <- range_read(ss = gs, sheet = "clima") %>%
mutate(date = as_date(Fecha))
str(met)
## tibble [180 × 6] (S3: tbl_df/tbl/data.frame)
## $ Fecha: POSIXct[1:180], format: "2022-09-01" "2022-09-02" ...
## $ TMax : num [1:180] 29 31.4 33.8 31.9 33.1 33.8 31.5 31.3 29.8 31.8 ...
## $ TMin : num [1:180] 14.8 14.1 14.8 16.2 16.8 15.6 16.1 15.8 15.4 15.4 ...
## $ HR : num [1:180] 70 69.1 65.1 66.7 64.6 67.3 68.2 67.9 70.2 71.4 ...
## $ PP : num [1:180] 0 0 0 0 0 0 0 0 0 0 ...
## $ date : Date[1:180], format: "2022-09-01" "2022-09-02" ...
names(met)
## [1] "Fecha" "TMax" "TMin" "HR" "PP" "date"
max(met$PP)
## [1] 107.9
scale <- 3
plot <- met %>%
ggplot(aes(x = date)) +
geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8) +
geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8) +
geom_bar(aes(y = PP/scale)
, stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8) +
scale_color_manual("", values = c("skyblue", "red", "blue")) +
scale_y_continuous(limits = c(0, 40)
, expand = c(0, 0)
, name = "Temperature (°C)"
, sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
) +
scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y", name = NULL) +
theme_minimal_grid() +
theme(legend.position = "top")
plot %>%
ggsave2(plot = ., "files/weather.jpg", units = "cm"
, width = 25, height = 15)
knitr::include_graphics("files/weather.jpg")